We evaluate the classification accuracy and loss performance of our proposed BFL scheme (with 5 consensus rounds) and compare it with two related schemes. The first one is blacka traditional FL scheme [1]}, where ESs work independently and each only aggregates the models of its associated MDs and then broadcasts the resulting model across devices.
The second one is a traditional BFL without P2P consensus [2]}, in which each ES runs an averaging algorithm by collaborating with its MDs to build its aggregated model, and then a random ES is selected as a leader that builds a global model based on its local aggregated model without conducting P2P consensus. Fig. REF  illustrates the performance when training the SVHN dataset, showing the considerable improvements in terms of higher accuracy and lower loss compared with the counterparts. Although the performance degrades when the dataset becomes non-IID, our consensus-based BFL scheme still outperforms other algorithms. The BFL scheme without consensus achieves a better training performance than the traditional FL scheme since its randomized leader ES selection avoid local model bias across the clients. Moreover, the performance gap between our scheme and the others becomes larger in the non-IID case which demonstrates the benefit of consensus-based model aggregation over existing approaches. The advantages of our scheme are also verified on the Fashion-MNIST dataset in both IID and non-IID settings, as indicated in Fig. REF . For example, in the IID setting, our consensus-based BFL scheme improves the accuracy rate by 8% and 14% in comparison with the traditional BFL scheme without consensus and the traditional FL scheme, respectively.
